Part 2 of this Towards Data Science series tackles something I've been curious about—using RL to get LLMs to show their work with verifiable reasoning steps. The gap between "sounds right" and "provably right" is exactly where a lot of trust issues with AI live Worth a read if you're interested in making model outputs more auditable.
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